Ultrasonic apparatus for estimating artery parameters

ABSTRACT

The invention relates to an ultrasonic image processing system, for processing images in an image sequence representing a segment of artery explored along its longitudinal axis, said artery segment showing moving walls; this system comprising: acquisition means ( 21 ) for acquiring an ultrasonic image sequence of a segment of artery explored along its longitudinal axis and having walls moving in relation with the cardiac cycle; semi-automatic detection means ( 22 ) for detecting the artery walls in an image of the sequence; automatic rigid tracking means ( 23 ) for tracking the corresponding artery walls in other images of the sequence; evaluation means ( 24 ) for evaluating the artery wall motion and distensibility; and viewing means ( 154 ) for visualizing images. The invention further relates to an ultrasound examination apparatus having a curved array of transducer elements and coupled to this system, having viewing means to visualize the images.

FIELD OF THE INVENTION

The invention relates to an ultrasonic imaging system and an ultrasonicexamination apparatus having processing means for constructing anddisplaying an ultrasonic examination image sequence of an artery segmentwith indications of arterial parameters in function of the cardiac cycleThe invention also relates to an image processing method having stepsfor operating this system and this apparatus. The invention is used inthe field of ultrasonic imaging, to provide a cardio-vascularnon-invasive medical tool for examining patients suspected to presentanomalies of arteries and notably anomalies of the aorta such as aorticaneurysms.

BACKGROUND OF THE INVENTION

An ultrasonic image processing method for calculating dilation curvesrelated to an artery segment is already known from the patent U.S. Pat.No. 5,579,771 (Bonnefous, Dec. 3, 1996). This document describes amethod for characterizing an artery segment by ultrasonic imaging, usingan array of ultrasonic transducers that produces a sectional frame,which is formed by image lines of a number of successive parallelexcitation lines extending perpendicularly to the artery axis. Saidarray is coupled to a transmitter/receiver circuit, which provides highfrequency signals to a signal processing system. Said system determinesthe arterial walls radial velocity and displacement amplitude values andfurther determines an arterial dilation curve in function of locationand time. Such a curve is constructed by points representing thearterial dilation value in the arterial radial direction Z, at a givenlocation corresponding to an excitation line along the longitudinalX-axis of the artery, in function of excitation instants t, during acardiac cycle. So, FIG. 4C of this document shows, superposed, thedifferent dilation curves related to all the excitation lines of anultrasonic signal corresponding to the examined artery segment, saidlines being at regularly spaced locations along the X-axis of theartery.

The dilation curves are certainly very useful for the study of stenoses.A problem is that, in fact, for the study of aneurysms, the evaluationof distensibility is more exploitable by a cardiologist. The severity ofan aneurysm may be estimated by considering its maximal diameter. Thus,the dilation information is useful. However, at the present time,cardiologists think that the mechanical stress acting on the arterywalls at the location of the aneurysm is a very appropriateconsideration. Thus, the distensibily information is a very appropriateconsideration and is preferably used together with the dilationinformation.

Another problem is that the cited document relates to an imageprocessing method based on image acquisition with ultrasound scanninglines that are perpendicular to the artery axis. This corresponds to theuse of an ultrasound system for acquiring the ultrasound data with alinear array of transducer elements. This kind of system is appropriatefor studying a shallow artery and a small segment of artery such as thecarotid. This kind of system is not appropriate for the study of a deepand thick artery such as the aorta and particularly for the study ofAbdominal Aortic Aneurysms (AAA). For studying the aorta and AAA, acurved array of transducer elements is preferably used. When theultrasound data are acquired with a curved array, then the methoddisclosed in the cited document for calculating artery dilations cannotbe directly used, since the scanning lines are no longer perpendicularto the artery axis.

Another problem is that the cited document only permits of disposing ofconstant values corresponding to the reference coordinates of the arterywalls at the instants of zero dilation. This corresponds to arepresentation of the artery wall by a straight reference line at theinstants of zero dilation. Such a linear reference representation isdifficult to understand for the clinician. Hence, there is a need for aprecise location and representation of the artery walls at theseinstants of zero dilation.

Abdominal Aortic Aneurysm (AAA) is defined by a doubling of the normalinfra-renal aortic diameter. In order to early diagnosing aneurysms inaorta, the medical field has a need for non-invasive means for providingaorta images together with clear quantified indications of the aorticdistensibility, which is a measure that is used by clinicians togetherwith dilation information.

SUMMARY OF THE INVENTION

In order to address the problem of finding new diagnosis information forthe follow up of patients suspected to present Abdominal AorticAneurysms (AAAs), it is an object of the invention to propose an imageprocessing system for the evaluation of parameters related to thetension and strain of the aneurysm walls. The present invention-proposesa system developed for AAAs that is specifically designed to provideclinicians with information on the motion of the aorta artery walls.

This image processing system is claimed in claim 1. This imageprocessing system offers the advantage that the aorta wall behavior ismade clearly visible together with the parameters that are useful forthe clinician in the study of these Abdominal Aortic Aneurysms. Thissystem has display means to visualize the images and constitutes a toolfor non-invasive diagnostic of arterial wall anomalies. An ultrasonicdiagnostic method having processing steps for operating this system andan ultrasound apparatus coupled to this system are claimed in dependentClaims.

BRIEF DESCRIPTION OF THE FIGURES

Specific embodiments of the invention will be described in detailhereinafter with reference to the accompanying diagrammatic drawings;therein:

FIG. 1A shows a schematic representation of an aorta and AbdominalAortic Aneurysm (AAA); FIG. 1B shows wall stress distribution on anAbdominal Aortic Aneurism;

FIG. 2 is a block diagram showing the main steps of the method of theinvention;

FIG. 3 image in the image sequence, with wall borders drawn in ROIP andROID;

FIG. 4 is a block diagram illustrating user interaction for drawing theartery wall borders;

FIG. 5 is an image of pixel costs for optimal path detection;

FIG. 6 illustrates the tracking propagation scheme;

FIG. 7 illustrates wall border tracking in the images of the sequence;

FIG. 8 is a block diagram of sub-steps of the tracking stage for findingwall borders using ROIP and ROID in the image sequence;

FIG. 9 is a block diagram of sub-steps of forward or backward rigidtracking in the sequence images;

FIGS. 10A and 10B are two views of the (2-D+t) potential function forROIP and ROID;

FIG. 11 is an ultrasound image with indications of interactive selectionof a diameter of an aorta for distensibility calculation;

FIG. 12 is an ultrasound image with indications of the dilations of anaorta;

FIG. 13 illustrates a box of information giving parameters related to asegment of aorta;

FIG. 14 is a block diagram of an examination apparatus with a viewingsystem having processing and display means for carrying out the methodof the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Referring to FIG. 1A, Abdominal Aortic Aneurysm AAA is defined by adoubling of the normal diameter of the infra-renal aorta A. The heart isdenoted by H. The AAA abnormality is present in 5% of men aged over 65years. Rupture of the aneurysm, the most common complication of AAA, isresponsible for about 2% of deaths in men in this age group and is thetenth leading cause of death in men in Europe. Since most AAAs areasymptomatic until rupture occurs, up to 50% of all AAAs repairs areperformed as an emergency operation. As the operative mortality forruptured AAA is around 50%, and only a small fraction of patients withruptured AAAs survive to reach hospital, the overall community mortalityfor ruptured AAAs is estimated at over 90%. For this reason, there is anincreasing interest in the clinical and cost effectiveness of massscreening programs for AAAs. Acquired abdominal aortic aneurysmsclassically are characterized anatomically by an unparallelism of theaorta edges, resulting in an expanded and beating abdominal mass. Thephysiopathology consists of a loss of vascular contention, including arisk of rupture. Indeed, the aorta fulfills several haemodynamicfunctions of blood tissue distribution, damping of the pulse wave, etc.The most elementary of these functions is containing high pressure bloodwithin the arterial lumen. Arterial wall aneurysmal diseases arecharacterized by partial loss of integrity called dilation or total lossof integrity corresponding to a rupture. Therefore, in order to earlydiagnosing aneurysms in aorta, the medical field has a need fornon-invasive means for providing aorta images together with clearquantified indications of the aortic distensibility. Besides, it isimportant to use non-invasive means instead of invasive means becauseinvasive means modifies the aorta pressure, hence the actual aortadistensibility.

The severity of Abdominal Aortic Aneurysm (AAAs) is generally clinicallyestimated by considering its maximal diameter. However, referring toFIG. 1B, which shows a wall stress distribution in different shades ofcolors on AAA shape, cardiologists tend to think that the mechanicalstress acting on the artery walls at the location of the aneurysm iscertainly a more appropriate consideration. Thus, distensibilyinformation is a more appropriate consideration than dilationinformation. In fact, it is known that failure of any material occurswhen the wall stress exceeds the strength of the material. Whereasoperative indications for elective AAA repair are generally based onaneurysm size greater than 4.5 to 5 cm in diameter, the most frequentlyused medical approach is watchful waiting, whereby aneurysm diameter isperiodically re-measured to detect expansion to a size warrantingsurgery of the patient. Now, it is also known that AAAs with a diameterless than 5 cm can rupture. Hence, there is a clear need for additionaldiagnosis information. Therefore, distensiblity information will bepreferably used.

The present invention proposes an image processing system and an imageprocessing method to provide aorta parameters for the evaluation of thetension and strain of the aneurysms walls. The system and the method aredeveloped for AAAs and are specifically designed to provide clinicianswith information on the behavior of the aortic artery walls.

The method is first described. This method permits of evaluatingautomatically, or with limited user interaction, and at any time in theimage sequence, the position of the artery walls, in order to estimatethe artery dilations and distensibility.

Referring to FIG. 2, the processing of an image sequence is divided intomain steps of:

-   -   1) Acquisition of the image sequence 21;    -   2) Semi-automatic detection 22 of the aorta proximal and distal        walls through user interaction combined with live-wire        detection. This step is applied to one frame of the sequence;    -   3) Automatic rigid tracking 23 of the two walls in the sequence;    -   4) Evaluation 24 of the artery wall motions and dilations.    -   5) Display 25 of the results in the sequence.

This Abdominal Aortic Aneurysm Wall Motion (AAAWM) tool particularlycomprises:

1) Acquisition 21 of a sequence of ultrasound images of a segment ofartery, for instance a segment of aorta, using a linear curved array.Said artery segment has a longitudinal axis and is represented ingrayscale images as illustrated by FIG. 3 or FIG. 11 or FIG. 12.

Referring to FIG. 14, an ultrasonic imaging system constructed inaccordance to the principles of the present invention is shown in ablock diagram form. In the example of embodiment that is describedhereafter, this ultrasonic imaging system is used as a tool for theexamination of the aorta. This ultrasonic imaging system comprisessub-systems to perform the image processing method of the invention forvisualizing the arterial segment whose walls have radial movements andfor quantifying its radial arterial dilation, which occurs under theinfluence of the blood pressure, at given locations of said arterialsegment and in function of the different time instants during a cardiaccycle.

2) Semi-automatic segmentation 22 of the artery walls, based on the echoinformation, in one image of the sequence. As illustrated by the blockdiagram of FIG. 4, the user interacts in one image of the sequence andperforms an assisted drawing of the artery boundaries using a techniquecalled live-wire technique. The user can draw the artery wall borders inany image of the sequence as illustrated by FIG. 3. Thus, as illustratedby the tracking propagation scheme of FIG. 6, the user can choose asstarting image, numbered n, where the definition of the boundariesappears with the best contrast. The live-wire technique includes theestimation of values called maxGrad and minGrad that representrespectively the maximum and minimum amplitude of the gradient in theimage. Since the echo image is noisy, the image is first smoothed, usinga gaussian filter, before gradient estimation. Then a cost function forthe live-wire technique, adapted for the delineation of the aortaboundaries, is defined.

3) Automatic rigid tracking 23 of the artery wall position in the restof the sequence, as illustrated by FIG. 7 and by the block diagrams ofFIG. 8 and FIG. 9. The tracking is started at the starting image wherethe user has drawn the artery wall boundaries. Referring to the trackingpropagation scheme of FIG. 6, if this starting image numbered n is inthe middle of the image sequence 4, a forward tracking 2 and a backwardtracking 1 are performed in order to provide a complete tracking for thewhole image sequence 4. Regarding the forward tracking 2, the principleis to initialize the position of a structure in the current frame (n+1)at the same position as found in the previous frame n and then to movethe structure in order to fit the boundaries of the current frame, asillustrated for example in FIG. 7. The movements of the structure arelimited to vertical and horizontal translations. In order to determinethe best fit in the current frame, an optimization criterion is used,based on the minimization of a cost function.

4) Additional processing 24 in order to measure the dilation and thedistensibility of abdominal aorta with the ultrasound system using alinear curved array.

5) Output 25 of the parameters related to the artery under study asillustrated for example by the box of FIG. 11. The method of theinvention can be used to measure the dilation of Abdominal AorticAneurysms (AAA) in the context of a surveillance of the growth, beforetreatment and after treatment with an endoprothesis. Local motiongradients show local strains. Low pulsatility in aneurysms is anindicator of non elastic arteries due to dilated walls and can be anindication of risk of rupture, which is a major health hazard. Highpulsatility after stenting show a reperfusion of the aneurysm indicatinga leak in the stent and necessitating further clinical intervention.

FIG. 14 shows a diagram of a medical viewing system 150 according to theinvention for carrying out the steps of the image processing methoddescribed hereafter. The system has means 151 for acquiring digitalimage data of a sequence of images, and is coupled to computer means 153for processing these data according to this image processing method. Thedata processing device 153 is programmed to implement a method ofprocessing medical image data according to invention. In particular, thedata processing device 153 has computing means and memory means toperform the steps of the method. A computer program product havingpre-programmed instructions to carry out the method may also beimplemented. Steps of the present method can be applied on storedmedical images, for example for estimating medical parameters. Themedical viewing system provides the image data by connection 157 to thesystem 153. The system provides processed image data to display meansand/or storage means. The display means 154 may be a screen. The storagemeans may be a memory of the system 153. Said storage means may bealternately external storage means. This image viewing system 153 maycomprise a suitably programmed computer, or a special purpose processorhaving circuit means such as LUTs, Memories, Filters, Logic Operators,that are arranged to perform the functions of the method steps accordingto the invention. The system 153 may also comprise a keyboard 155 and amouse 156. Icones may be provided on the screen to be activated bymouse-clicks, or special pushbuttons may be provided on the system, toconstitute control means 158 for the user to actuate the processingmeans of the system at chosen stages of the method. This medical viewingsystem 150 may be incorporated in an ultrasound examination apparatus151. This medical examination apparatus 151 may include a bed on whichthe patient lies or another element for localizing the patient relativeto the apparatus. The image data produced by the ultrasound examinationapparatus 151 is fed to the medical viewing system 150.

The technical implementation of the above described steps for formingthe Abdominal Aortic Aneurysm Wall Motion (AAAWM) tool is describedhereafter more precisely. The terms artery wall border and “structure”have the same meaning and represent a segmented object.

As disclosed by the prior art cited in the introduction part, it isalready known to determine artery dilations using a linear probe appliedto the carotid artery of a patient. The known method is no moreappropriate for carrying out the present method, since the probe usedfor the examination of the aorta of a patient is curved. The aorticaneurysms have very varying shapes and sizes depending on the subjectunder examination. As a consequence, the detection of the walls(structures) in the images of a sequence requires a very adaptivesegmentation tool. In order to deal with the variability of the images,it is preferable to combine user interaction with a more automaticprocessing for the segmentation of an image. Thus, the user is asked todelineate the boundaries of the AAA walls in one image of the sequencethat he can select. This delineation is semi-automatic and is based on atechnique called “Live-Wire”, which is described in a publicationentitled “User-Steered Image Segmentation Paradigms: Live Wire and LiveLane” by A. X. Falcao, J. K. Udupa, S. Samarasekera, S. Sharma, and B.E. Hirsh, in “Graphical Models and Image Processing 60, pp. 233-260,1998”. The principle of the method of the present invention is toprovide the automatic detection of a boundary located between successivepoints selected by the user on this boundary. The boundary detection isbased on the optimization of a cost function.

Implementation of Step 1: Acquisition of an Image Sequence.

As a matter of example, the processed sequence of abdominal aorticaneurysms (AAA) has been acquired with a Tissue Doppler Imaging (TDI)modality, using a C5-2 probe and a Philips HDI5000 scanner.

Implementation of Step 2: Semi-Automatic Edge Detection in One SelectedFrame.

Referring to FIG. 14, and to FIG. 3, for assisted-drawing of a boundary,the user can click on the left or right button of the mouse 156 and movethe mouse, while visualizing the starting image selected in the sequenceof images numbered n that is displayed on the screen 154. Referring toFIG. 4, the left clicks 41 are used to begin a boundary and to selectintermediate points in a boundary. The right clicks 47 are used toterminate a boundary. The boundaries are stored in “path” structures.The handling of the different user interactions is described bellow:

With the left click 41, if it is the first click: creation 42 of a newpath structure; else: addition 43 of the temporary path to the pathstructure.

With the mouse move 44: Finding 45 the optimal path between the lastleft click and the current cursor position; or filling 46 a temporarypath with the result.

With the right click 47: Addition 48 of the temporary path to the pathstructure; or finishing 49 the path.

A cost function is used to determine the optimal path between twosuccessive positions of the mouse. The first position is alwaysassociated to a click of the user. The second position can either be thecurrent position of the mouse or a click of the user. This allows toshowing to the user in real-time where the optimal path is found by thepath search technique. The cost of a path between two positions of themouse is the sum of the costs of the individual pixels that constitutethe path. Since the goal of the path search technique is to minimize thecost of a path, the costs of the individual pixels at boundary positionsmust be small. The individual costs are based on the gradient of theecho image. Since the echo image is rather noisy, it is first smoothed,using a gaussian filter, before the gradient estimation. The cost of apixel is defined by the following formula: $\begin{matrix}{{\cos\quad{t\left( {pixel}_{i} \right)}} = {255*\frac{{\max\quad{Grad}} - {{grad}\left( {pixel}_{i} \right)}}{{\max\quad{Grad}} - {\min\quad{Grad}}}}} & (1)\end{matrix}$where maxGrad and minGrad represent respectively the maximum and minimumamplitude of the gradient. The cost of individual pixels is calculatedfor each pixel of the image where the user interacts. FIG. 5 shows animage of pixel costs for optimal path detection, where low costs are indark and represent image boundaries.

Implementation of Step 3: Structure Rigid Tracking in the Image SequenceS.

In fact, the aortic aneurysms do not considerably deform through animage sequence. As a first approximation, a rigid tracking of themotion, limited to translations, can be used to automatically detect thestructures in the remainder of the sequence. The tracking is initializedwith the result of the semi-automatic segmentation provided by the userin the initially selected frame of the sequence. The proximal and thedistal walls, also called structures, are individually tracked in thewhole sequence.

Referring to FIG. 8, the rigid tracking comprises general sub-stepsamong which:

-   -   Sub-steps 81 and 82 of drawing proximal and distal wall borders,        called structures, in the selected Frame n, called starting        Frame;

Sub-steps 83, 84 of defining regions of interest, denoted by ROI, aroundeach structure: FIG. 3 illustrates the definition of one ROI called ROIPfor the determination of the proximal wall border denoted by P1, and thedefinition of one ROI called ROID for the determination of the distalwall border denoted by P2. Same ROIs are used in all the frames of thesequence;

-   -   Sub-steps 85, 86 of tracking the proximal and distal wall        borders in ROIP and ROID respectively.

Referring to FIG. 7 and FIG. 9, the rigid tracking is initialized insub-step 91 with the structures semi-automatically segmented in theselected frame denoted by n of the sequence S. The tracking starts fromthe selected frame n and is propagated towards the beginning of thesequence according to a direction 1 called backward tracking, as well astowards the end of the sequence according to a direction 2 calledforward tracking as illustrated by the scheme of FIG. 6. The iterationof the rigid tracking for one structure in a sequence is described withreference to FIG. 6 and FIG. 9. This description is limited to theforward tracking 2. The technique for backward tracking 1 is fullysymmetric. The rigid tracking comprises detailed sub-steps of:

In a sequence S, selection 91 of a starting Frame n in the imagesequence 4 and drawing a path as previously described in reference toStep 2 illustrated by FIG. 4 and detailed sub-steps 41 to 49;

Referring to FIG. 7, using the position P of the path in Frame n, as aninitial estimate of the position of the path with the coordinates X, Y,performing an estimation 92 of the path P′ in the next frame (n+1); theprinciple is to initialize, in sub-step 92, the position of a structurein the current frame (n+1) at the same position as found in the previousframe n and then to move the structure in order to fit the boundaries ofthe current frame;

Evaluation 93 of the cost of the path at the current position in frame(n+1) as the sum of the potentials of each point of the path; in orderto determine the best fit in the current frame, an optimizationcriterion is defined. This criterion is the minimization of a costfunction in sub-step 93. Similarly to the principle used in previousstep 2, the cost of a structure is defined as the sum of the costs ofall the pixels of the structure;

Finding 94 the translation, among a limited number of possibletranslations, that minimizes the cost of the path; the search for theoptimal translation is implemented with a full exploration of thepossible translations within the limits of allowed translations insub-step 94; the movements of the structure are limited to vertical andhorizontal translations;

Moving 95 the path by the optimal translation found at the previousstep; the hypothesis of motion continuity is used to reduce the areawhere the translations are considered in sub-step 95;

Iteration 96 from the sub-step 92 of path estimation until the end ofthe sequence.

The cost function used for the spatio-temporal tracking of thestructures in the sequence is based on individual pixel costs calculatedas in equation (1). The main difference is that the gradient iscalculated for all the frames of the sequence and that these frames areconsidered as a two-dimensional (X,Y)+time (t) volume [(2-D+t) volume]and not as individual frames. This provides a spatio-temporal estimationof the gradient in the sequence. This technique is interesting becauseit smoothes the gradient in time direction, which ensures more motioncontinuity between successive frames. Since the computation of the(2-D+t) gradient is the most time consuming step of the whole processingof the AAAWM tool, this computation is performed in the regions ofinterest denoted by ROIP for the proximal wall border determination, andROID for the distal wall border determination. Same ROIs are used in allthe frames of the sequence and thus defines the (2-D+t) image. Costimages for ROIP and ROID are represented respectively in FIGS. 10A and10B, which show 2-D views of the (2-D+t) potential function for eachROI.

Implementation of Step 4: Evaluation of the Artery Dilations.

The ultrasound color information used to process the wall motion is theultrasound raw color data. It is composed of the lines of the ultrasoundcolor scanning and, for each line, the estimates of velocities in depth.The distensibility is interactively measured by selecting two oppositepoints on the arterial walls in an image. The two points are linked bysegment 11, illustrated as shown in FIG. 11, to represent the diameterat the selected position. A pre-requisite for the evaluation of thedistensibility d, is that the dilations of the artery walls have beencomputed, as illustrated by FIG. 11 and FIG. 12.

The dilation estimation is the result of the difference of motionbetween two structures for each ultrasound color line. The dilations arecalculated, as disclosed in the document cited as prior art, in order toprovide input data for the interface of the application, as illustratedby the image of FIG. 12 and the box of FIG. 13. The distensibility d iscomputed at the selected diameter position using the following formula:$\begin{matrix}{d = {\frac{dilation}{diameter}*100\%}} & (2)\end{matrix}$

Implementation of Step 5: Display of the Images and Parameters.

In order to represent the motion in the images, a choice must be maderegarding the estimated direction of the motion. In this application,the hypothesis is that the motion of the artery walls is perpendicularto the artery principal axis. The display provided in each frame of thesequence is limited to two types of information. The first type is thestructure location. The proximal and distal walls, called structures 12,are represented in colors, preferably in the same color, called firstcolor. Then, the motion of each wall along each ultrasound color line ispreferably represented in another color, called second color. Thereference line for a null motion is the structure itself and theamplitudes are represented starting from the structure position. Therepresentation of the lines of the second color allows to understandingthe direction of projection that was selected for each motion amplitude.The lines of the second color are interconnected to represent theoverall shape 13 of the motion between ultrasound color lines. After theprocessing, the results are summarized on a dedicated interface, such asin FIG. 12 and FIG. 13. FIG. 12 represents an echo image in gray levelcorresponding to the user-selected frame, combined with the segmentationresult and the dilation amplitudes. The image of FIG. 12 also representsdilation amplitudes. The box of FIG. 13 displays the artery parameters.

1. Ultrasonic image processing system, for processing images in an imagesequence representing a segment of artery explored along itslongitudinal axis, said artery segment showing moving walls; this systemcomprising: semi-automatic detection means for detecting the arterywalls in an image of the sequence; automatic rigid tracking means fortracking the corresponding artery walls in other images of the sequence;evaluation means for evaluating the artery wall motion anddistensibility; and viewing means for visualizing images.
 2. The systemof claim 1, wherein the semi-automatic detection means is a userassisted artery wall detection means comprising user interaction meansfor: selecting a reference image as starting image among the images ofthe sequence; drawing lines, called paths, representing the artery wallsin the starting image, assisted by a path search technique based on theminimization of a cost function.
 3. The system of claim 2, wherein theuser interaction means for drawing a path representing a wall comprisesmeans for: selecting a starting pixel in the starting image for creatinga new path structure; drawing a portion of path between the startingpixel and a second selected pixel in the starting image; evaluating thecost function of the portion of path as the sum of the cost of theindividual pixels that constitute the path; selecting the optimal pathas the path that minimizes the cost function; memorizing the optimalpath; drawing portions of path between successive pixels; and performingthe operations of evaluating the respective cost functions, selectingthe optimal paths and memorizing the optimal paths until a complete pathis drawn for the artery wall.
 4. The system of claim 3, having means forestimating the cost of the individual pixels based on the gradient atthe pixel in the ultrasonic image.
 5. The system of one claim 3, whereinthe automatic rigid tracking means for tracking the corresponding arterywalls in other images of the sequence comprises means of path findingincluding means for: defining regions of interest (ROIP, ROID) aroundthe paths drawn in the starting image and using the same regions ofinterest in the other images of the sequence; selecting a current imagenext to the starting image; initializing the tracking of the paths inthe current image by using the positions of the paths in the startingimage; applying translations to said initial paths in the current imageto fit the walls in the current image; evaluating the cost of the pathsin the current image using the same cost function as in the startingimage and finding the translations that minimizes the cost function;iterating these path finding steps until the beginning and the end ofthe sequence are reached.
 6. The system of claim 5, wherein means forevaluating the cost performs cost evaluation of the individual pixelsbased on the gradient at the pixel in the ultrasonic images calculatedfor all the images of the sequence, considered as a two-dimensional plustime volume.
 7. The system of one of claim 1, comprising computationmeans for calculating the dilation of the artery along the ultrasoundbeams in the images of the sequence using the segmentation of the wallsperformed by path finding with semi-automatic detection and rigidtracking.
 8. The system of claim 7, comprising computation means forcalculating the distensibility as the ratio of the dilation by thediameter of the artery.
 9. A system as claimed in claim 1, having colordisplay means to display colored paths for the artery walls and coloredpatterns for the wall dilation, superimposed on the ultrasonic images.10. A system as claimed in claim 1, comprising a suitably programmedcomputer of a workstation or a special purpose processor having circuitmeans, which are arranged to process ultrasonic images, having means todisplay the processed images, and having a user interface such as amouse or a keyboard to permit the user of interacting on the respectiveimages of the sequence in order to display the quantified parametersrelated to the artery walls.
 11. A computer program product comprising aset of instructions to be used in a system as claimed claim
 1. 12. Asystem as claimed in claim 1, wherein the transducer array is a curvedtransducer array.
 13. An ultrasonic medical image processing methodcomprising steps of acquiring a sequence of ultrasound images, using anarray of transducer elements, and steps for detecting anomalies inarteries comprising: semi-automatic detection of the artery walls in areference image of the sequence; automatic rigid tracking of thecorresponding artery walls in other images of the sequence; evaluationof the artery wall motion and distensibility; and visualization ofimages.
 14. An ultrasound examination apparatus having means to acquiredultrasound images and coupled to an image processing system according toclaim 1.